• Corpus ID: 222291715

Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning

@article{Naderializadeh2020GraphCV,
  title={Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning},
  author={Navid Naderializadeh and Fan Hung and Sean Soleyman and Deepak Khosla},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.04740}
}
We propose a novel framework for value function factorization in multi-agent deep reinforcement learning using graph neural networks (GNNs). In particular, we consider the team of agents as the set of nodes of a complete directed graph, whose edge weights are governed by an attention mechanism. Building upon this underlying graph, we introduce a mixing GNN module, which is responsible for two tasks: i) factorizing the team state-action value function into individual per-agent observation-action… 
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References

SHOWING 1-10 OF 40 REFERENCES
Value-Decomposition Networks For Cooperative Multi-Agent Learning
TLDR
This work addresses the problem of cooperative multi-agent reinforcement learning with a single joint reward signal by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions.
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
TLDR
This work proposes a 'permutation invariant critic' (PIC), which yields identical output irrespective of the agent permutation, which enables the model to scale to 30 times more agents and to achieve improvements of test episode reward between 15% to 50% on the challenging multi-agent particle environment (MPE).
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
TLDR
A new factorization method for MARL, QTRAN, is proposed, which is free from such structural constraints and takes on a new approach to transforming the original joint action-value function into an easily factorizable one, with the same optimal actions.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
TLDR
An adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination is presented.
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
TLDR
This work presents an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep, which enables more effective and scalable learning in complex multi- agent environments, when compared to recent approaches.
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
TLDR
QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations, and structurally enforce that the joint-action value is monotonic in the per- agent values, which allows tractable maximisation of the jointaction-value in off-policy learning.
The StarCraft Multi-Agent Challenge
TLDR
The StarCraft Multi-Agent Challenge (SMAC), based on the popular real-time strategy game StarCraft II, is proposed as a benchmark problem and an open-source deep multi-agent RL learning framework including state-of-the-art algorithms is opened.
Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms
TLDR
This work evaluates and compares three different classes of MARL algorithms in a diverse range of multi-agent learning tasks and shows that algorithm performance depends strongly on environment properties and no algorithm learns efficiently across all learning tasks.
Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception…
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
TLDR
By embracing deep neural networks, this work is able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability.
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